In [22]:
# studying the distribution of continuous attributes
cols = list(mydata)
for i in np.arange(len(cols)):
sns.distplot(mydata[cols[i]], color='blue')
#plt.xlabel('Experience')
plt.show()
print('Distribution of ',cols[i])
print('Mean is:',mydata[cols[i]].mean())
print('Median is:',mydata[cols[i]].median())
print('Mode is:',mydata[cols[i]].mode())
print('Standard deviation is:',mydata[cols[i]].std())
print('Skewness is:',mydata[cols[i]].skew())
print('Maximum is:',mydata[cols[i]].max())
print('Minimum is:',mydata[cols[i]].min())
Distribution of Parameter 1 Mean is: 8.319637273295838 Median is: 7.9 Mode is: 0 7.2 Name: Parameter 1, dtype: float64 Standard deviation is: 1.7410963181277006 Skewness is: 0.9827514413284587 Maximum is: 15.9 Minimum is: 4.6
Distribution of Parameter 2 Mean is: 0.5278205128205131 Median is: 0.52 Mode is: 0 0.6 Name: Parameter 2, dtype: float64 Standard deviation is: 0.17905970415353498 Skewness is: 0.6715925723840199 Maximum is: 1.58 Minimum is: 0.12
Distribution of Parameter 3 Mean is: 0.2709756097560964 Median is: 0.26 Mode is: 0 0.0 Name: Parameter 3, dtype: float64 Standard deviation is: 0.19480113740531785 Skewness is: 0.3183372952546368 Maximum is: 1.0 Minimum is: 0.0
Distribution of Parameter 4 Mean is: 2.5388055034396517 Median is: 2.2 Mode is: 0 2.0 Name: Parameter 4, dtype: float64 Standard deviation is: 1.4099280595072805 Skewness is: 4.54065542590319 Maximum is: 15.5 Minimum is: 0.9
Distribution of Parameter 5 Mean is: 0.08746654158849257 Median is: 0.079 Mode is: 0 0.08 Name: Parameter 5, dtype: float64 Standard deviation is: 0.047065302010090154 Skewness is: 5.680346571971724 Maximum is: 0.611 Minimum is: 0.012
Distribution of Parameter 6 Mean is: 15.874921826141339 Median is: 14.0 Mode is: 0 6.0 Name: Parameter 6, dtype: float64 Standard deviation is: 10.46015696980973 Skewness is: 1.250567293314441 Maximum is: 72.0 Minimum is: 1.0
Distribution of Parameter 7 Mean is: 46.46779237023139 Median is: 38.0 Mode is: 0 28.0 Name: Parameter 7, dtype: float64 Standard deviation is: 32.89532447829901 Skewness is: 1.515531257594554 Maximum is: 289.0 Minimum is: 6.0
Distribution of Parameter 8 Mean is: 0.9967466791744831 Median is: 0.99675 Mode is: 0 0.9972 Name: Parameter 8, dtype: float64 Standard deviation is: 0.0018873339538425559 Skewness is: 0.07128766294927483 Maximum is: 1.00369 Minimum is: 0.99007
Distribution of Parameter 9 Mean is: 3.311113195747343 Median is: 3.31 Mode is: 0 3.3 Name: Parameter 9, dtype: float64 Standard deviation is: 0.15438646490354266 Skewness is: 0.19368349811284427 Maximum is: 4.01 Minimum is: 2.74
Distribution of Parameter 10 Mean is: 0.6581488430268921 Median is: 0.62 Mode is: 0 0.6 Name: Parameter 10, dtype: float64 Standard deviation is: 0.16950697959010977 Skewness is: 2.4286723536602945 Maximum is: 2.0 Minimum is: 0.33
Distribution of Parameter 11 Mean is: 10.422983114446502 Median is: 10.2 Mode is: 0 9.5 Name: Parameter 11, dtype: float64 Standard deviation is: 1.0656675818563965 Skewness is: 0.8608288069184189 Maximum is: 14.9 Minimum is: 8.4
Distribution of Signal_Strength Mean is: 5.6360225140712945 Median is: 6.0 Mode is: 0 5 Name: Signal_Strength, dtype: int64 Standard deviation is: 0.8075694397347023 Skewness is: 0.21780157547366327 Maximum is: 8 Minimum is: 3
